PseudoSegRT: efficient pseudo-labelling for intraoperative OCT segmentation

Wide-field OCT volumetric segmentation using semi-supervised CNN and transformer integration

Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in the retina, beyond the conventional fields of view used in current clinical OCT imaging systems. However, wide-field scans can present significant challenges for retinal layer segmentation. Deep Convolutional Neural Networks (CNNs) have shown strong performance in medical imaging segmentation but typically require large-scale, high-quality, pixel-level annotated datasets to be effectively developed. To address this challenge, we propose an advanced semi-supervised learning framework that combines the detailed capabilities of convolutional networks with the broader perspective of transformers. This method efficiently leverages labelled and unlabelled data to reduce dependence on extensive, manually annotated datasets. We evaluated the model performance on a dataset of 74 volumetric OCT scans, each performed using a prototype swept-source OCT system following a wide-field scan protocol with a 15 × 9 mm field of view, comprising 11,750 labelled and 29,016 unlabelled images. Wide-field retinal layer segmentation using the semi-supervised approach show significant improvements (P-value < 0.001) of up to 11% against a UNet baseline model. Comparisons with a clinical spectral-domain-OCT system revealed significant correlations of up to 0.91 (P-value < 0.001) in retinal layer thickness measurements. These findings highlight the effectiveness of semi-supervised learning with cross-teaching between CNNs and transformers for automated OCT layer segmentation.

Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends

PURPOSE OF REVIEW

Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology.

RECENT FINDINGS

Some recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration.

SUMMARY

Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.

Methods for real-time feature-guided image fusion of intrasurgical volumetric optical coherence tomography with digital microscopy

4D-microscope-integrated optical coherence tomography (4D-MIOCT) is an emergent multimodal imaging technology in which live volumetric OCT (4D-OCT) is implemented in tandem with standard stereo color microscopy. 4D-OCT provides ophthalmic surgeons with many useful visual cues not available in standard microscopy; however it is challenging for the surgeon to effectively integrate cues from simultaneous-but-separate imaging in real-time. In this work, we demonstrate progress towards solving this challenge via the fusion of data from each modality guided by segmented 3D features. In this way, a more readily interpretable visualization that combines and registers important cues from both modalities is presented to the surgeon.